Title: A clustering-based simplification of massive automobile-bodies point cloud for lightweight design

Authors: Yu Zhou; Yue Song; Qi Zhang; Yan Wang; Fa-rong Du; Shui-ting Ding

Addresses: Research Institute of Aero-Engine, Beihang University, Beijing, 100191, China; Beijing Key Laboratory for High-efficient Power Transmission and System Control of New Energy Resource Vehicle, Beihang University, Beijing, 100191, China ' School of Energy and Power Engineering, Beihang University, Beijing, 100191, China; Aircraft/Engine Integrated System Safety Beijing Key Laboratory, Beihang University, Beijing, 100191, China ' Beijing Key Laboratory for High-efficient Power Transmission and System Control of New Energy Resource Vehicle, Beihang University, Beijing, 100191, China; School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China ' Beijing Key Laboratory for High-efficient Power Transmission and System Control of New Energy Resource Vehicle, Beihang University, Beijing, 100191, China; School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China ' Research Institute of Aero-Engine, Beihang University, Beijing, 100191, China; Aircraft/Engine Integrated System Safety Beijing Key Laboratory, Beihang University, Beijing, 100191, China ' Research Institute of Aero-Engine, Beihang University, Beijing, 100191, China; Aircraft/Engine Integrated System Safety Beijing Key Laboratory, Beihang University, Beijing, 100191, China

Abstract: Adaptive simplification for massive and large-scale automobile-bodies point cloud obtained by 3D laser-scanning has been proven to be an effective technology to conduct lightweight design. This paper introduces a point-based algorithm to simplify laser-scanning point cloud without any support of fitted surface. The intrinsic characteristic of laser-scanning data is investigated to produce a topological connectivity for adjacent points in scanlines. We explore an automatic normal-vector estimation framework through the relationship between normal-vector and its adjacent geometric elements. To retain more points in high-curvature areas and fewer points in planar regions efficiently, the local normal-vector variance is adopted to determine subdivision-decision condition. The boundary points are detected and then preserved before non-uniform subdivision. A relevant simplification system based on our algorithm is developed. Many simplification cases are implemented to validate the effectiveness of our method and demonstrate the feasibility for automobile-bodies point cloud. The comparison with other point-based methods is also performed to illustrate the superiority of our method.

Keywords: point cloud; simplification; laser scanning; lightweight design; automobile body; hierarchical clustering; reverse engineering; non-uniform subdivision; boundary-points preservation; curvature awareness.

DOI: 10.1504/IJVD.2022.127013

International Journal of Vehicle Design, 2022 Vol.88 No.2/3/4, pp.177 - 201

Accepted: 07 Apr 2021
Published online: 18 Nov 2022 *

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